Comprehensive characterization of cardiac contraction for improved post-infarction risk assessment

This study aims at identifying risk-related patterns of left ventricular contraction dynamics via novel volume transient characterization. A multicenter cohort of AMI survivors (n = 1021) who underwent Cardiac Magnetic Resonance (CMR) after infarction was considered for the study. The clinical endpoint was the 12-month rate of major adverse cardiac events (MACE, n = 73), consisting of all-cause death, reinfarction, and new congestive heart failure. Cardiac function was characterized from CMR in 3 potential directions: by (1) volume temporal transients (i.e. contraction dynamics); (2) feature tracking strain analysis (i.e. bulk tissue peak contraction); and (3) 3D shape analysis (i.e. 3D contraction morphology). A fully automated pipeline was developed to extract conventional and novel artificial-intelligence-derived metrics of cardiac contraction, and their relationship with MACE was investigated. Any of the 3 proposed directions demonstrated its additional prognostic value on top of established CMR indexes, myocardial injury markers, basic characteristics, and cardiovascular risk factors (P < 0.001). The combination of these 3 directions of enhancement towards a final CMR risk model improved MACE prediction by 13% compared to clinical baseline (0.774 (0.771—0.777) vs. 0.683 (0.681—0.685) cross-validated AUC, P < 0.001). The study evidences the contribution of the novel contraction characterization, enabled by a fully automated pipeline, to post-infarction assessment.


SUPPLEMENTAL MATERIAL I -LV Segmentation
A 2-step state-of-art approach was applied to segment the LV endo-and epicardium (see Fig. SI.1), from which the LV cavity and myocardium can be estimated (32,33):  In the first step, pre-processing, the images are re-oriented, cropped and normalized.Considering the mid-ventricular SAx slice as reference, the first neural network (NN) detects the position of the heart and defines a region of interest (ROI) of 139.7x139.7 mm centered in the LV.Based on these LV centroid and ROI, the LV is aligned to a canonical position by a rigid registration of the same SAx reference slice to the atlas built in (33).The SAx is then cropped accordingly, and the intensities are normalized.
 The second step, fine segmentation, applies another NN to the pre-processed images to regress the enhanced LV segmentation.(top) along with a step-by-step explanatory illustration of its application on patient S221 (bottom).This pipeline design addresses canonical orientation for LV regional metrics quantification and label imbalance for segmentation performance improvement.See (33) for further details.
A cohort of 100 patients of the study, manually segmented ,with random resolutions and endpoints, was used to train the 2 NNs, following a 5-fold cross-validation strategy and using the same trainingvalidation-testing split ratio as in (33).Translation, rotation and flipping were used for augmentation.Architectures and implementation are detailed in (32,33).Segmentation performance assessment is based on endocardium and epicardium gold standard Dice scores.This metric accounts for the overlap between manual segmentation and automated prediction and varies between 0 and 1, with 1 corresponding to a perfect match.Reproduced from (32).

SUPPLEMENTAL MATERIAL II -Volume Transient Normalization
The LV volume temporal transients are described by the magnitude of the curve (i.e.maximum and minimum LV volumes) and by its shape (i.e.relative filling contributions, presence or absence of diastasis, etc.).While the latter rather involves subtle changes and its understanding is the aim of this study, the former, that contains information about ventricle size and amplitude of contraction or stroke volume, is a major source of variability and can be completely described by the well-established EDV, ESV and LVEF markers.This motivates the standardization in magnitude, to remove cofounding factors, towards facilitating multivariate models, and variability noise, so that the PCA analysis concentrates on these subtle transient shape changes.Thus, a standard minmax normalization is proposed: where ( ) represents the original LV volume transient (mL/s); ( ), the normalized LV volume transient (%/s); , the end-systolic volume (mL); and , the stroke volume (mL), calculated as endsystolic minus end-diastolic volume.Actual transients (mL vs s)

SUPPLEMENTAL MATERIAL III -Dimensionality Reduction
The LV volume temporal transients are obtained by LV cavity volume integration in each frame of the cardiac cycle, as explained in the manuscript (see Methods).Therefore, the transients are described as a collection of 2D points, where the 'x' dimension corresponds to the trigger time of the frame, or time-point within the cardiac cycle, and the 'y' dimension represents the integrated cavity volume.Given the multicenter and multi-scanner nature of the study, patients are imaged at different resolutions, which results in 2D collection of points of different sizes (i.e. from 20 frames, and, in consequence, 20 pairs of time and volume 2D points, to 50 frames).Thus, a first step resamples the obtained volume transients, via splines, to ensure that all of them are described with the same number of points, that is, 30 points (evenly distributed in the 'x' or time dimension).This effectively means that the LV volume transient of each patient is described by 60 variables (30 points x 2 dimensions), which take a certain different value per patient.A second step normalizes the 'y' or volume dimension, as explained in Supplemental Material II, to standardize in ventricle size and SV.  3) in order to maximize MACE (red) vs No MACE (blue) differences, that it, Vt AI 3 and Vt AI 5. We moved from 60 to just 2 transient variables per patient, which, additionally, are interpretable.
PCA, the dimensionality reduction technique applied in this work, is able to encode the information contained in these 60 variables (LV volume transient variability) into a few variables, the PCA modes, that describe the main LV volume transient variations.Mathematically, this is done by finding the vector space whose basis are the orthogonal directions that maximize the variance of the data, and subsequently projecting the data in this new space (See Fig. SIII.1).These directions that maximize the variance are, precisely, the PCA modes of variation that represent the way in which the LV volume transient varies in the population (i.e.RR-interval length, passive vs active filling relative contributions, etc.).As illustrated in

PCA data matrix
Projections in the PCA Space a particular shape variation, which has a certain value for each patient, , and whose MACE predictive power can be analyzed.In other words, we can investigate which LV transient features or contraction patterns (modes) are related to AMI prognosis.
The modes are sorted in descending order of importance according to the amount of variability of the population that they explain (i.e. in our results 51.6% variability is described by mode 1; 18.4%, by mode 2; etc.).As we progressively incorporate modes, the LV transient reconstruction improves to the point that with only few modes we can accurately approximate any volume transient (i.e.95.99% of LV transient variance described by first 6 modes), and hence the dimensionality reduction is achieved (See Fig. SIII.3).

Fig. SIII.2 -
The PCA direction of maximum variability corresponds to mode 1.In our results, the shape variation that it encodes is interpreted as mainly RR-interval length, although some other subsequent changes in transient morphology (i.e.diastasis) can be appreciated.As we move along the direction of mode 1, the RR-interval of the mean LV volume transient is reduced (positive Vt AI 1) or increased (negative Vt AI 1), proportionally to the value of its PCA coefficient Vt AI 1.

Fig. SIII.3
-Cumulative % of the population variance explained by the modes.As we incorporate more modes, we account for more variance and we accurately approximate the target shape (Patient 692).The first few modes account for the majority of the variance, and as we move to latter modes the improvement in reconstruction is smaller (i.e.mode 6 vs mode 7).Coefficients and constant of the resulting LDA models following the backward stepwise variable selection, to assess the additional prognostic contribution of LV contraction unravelling via conventional (Vt) and AI-derived (Vt AI ) volume transient; CMR-FT strains (strains); and LV 3D detail patterns (LV 3D ) metrics on top of considering only CMR biomarkers (CMR) or all the cardiovascular risk factors and patient characteristics of the study (ALL).All the variables are normalized to zero mean and unit variance prior to LDA fitting.The performance of these models is reported in the main manuscript (Table 3).Hazard ratios, HR (95% confidence interval), and predictor significance, P-value, of the resulting Cox multivariate models following the backward stepwise variable selection, to assess the additional prognostic contribution of LV contraction unravelling via conventional (Vt) and AI-derived (Vt AI ) volume transient; CMR-FT strains (strains); and LV 3D detail patterns (LV 3D ) metrics on top of considering only CMR biomarkers (CMR) or the cardiovascular risk factors and patient characteristics of the study (ALL).The performance of these models is summarized in the manuscript (Table 3).  3 and Table SV.1).The labels are presented as subgroup (MACE vs No MACE cases within the subgroup).

Fig. SV.3 -AUC endpoints prediction results
stratifying by infarct aetiology (STEMI vs NSTEMI) and applying the LDA models resulting from the backward stepwise analysis (See Table 3 and Table SV.1).
The labels are presented as subgroup (MACE vs No MACE cases within the subgroup).

SUPPLEMENTAL MATERIAL VII -Systolic vs Diastolic Components
The LV volume temporal transients are split into their systolic and diastolic components using the systolic time as reference.The analysis is repeated for each of the two components independently as described in Methods and Supplemental Material III.
The 95% of the population variance was explained by the first 4 systolic modes of variation (Vt-S AI ) and the first 5 diastolic modes of variation (Vt-D AI ), respectively.Among them, the LDA stepwise analysis determined modes 2, 4 and 5 (Vt-D AI 2, Vt-D AI 4 and Vt-D AI 5) as relevant to MACE in the diastolic component analysis; and only mode 2 (Vt-S AI 2,) in the systolic component scenario (see Fig. Nevertheless, only the diastolic component contributes to a significant improvement in prediction performance.This is in line with the results of the entire transient analysis (Vt AI ), presented in the main manuscript (see Table 3), where the two modes that contributed the most to the multivariate models were related to diastolic function, that is, Vt AI 3 and Vt AI 5.It is also sensible that the inclusion of the entire transient provides more additional prognostic value than any of the two components individually analyzed.

Continued
Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence interval).Make clear which confounders were adjusted for and why they were included Fig. SI.2 illustrates the architecture of this fine-segmentation network.A final postprocessing is applied to binarized the segmentation predictions and improve the segmentation quality.

Fig. SI. 1 -
Fig. SI.1 -Scheme of the proposed 2-step segmentation pipeline (top) along with a step-by-step explanatory illustration of its application on patient S221(bottom).This pipeline design addresses canonical orientation for LV regional metrics quantification and label imbalance for segmentation performance improvement.See (33) for further details.
Fig. SI.3 provides a visual sample of the segmentation results.

Fig. SI. 2 -
Fig. SI.2 -Graphical overview of the convolutional neural network structure, skip connections following a U-Net architecture, that is applied to achieve the fine-segmentation (2 nd pipeline step).Reproduced from (32).

Fig. SI. 3 -
Fig. SI.3 -Segmentation results of a representative patient (median Dice) at ED.The green contours correspond to the LV reference segmentation (manual segmentation); and the red contours, to the prediction results based on our proposed 2-step deep learning approach.

Fig
Fig. SII.1 illustrates two LV volume temporal transients from patients of a similar ventricle size before and after the proposed normalization.The intra-relationships within a transient are preserved,that is, the active versus passive contribution balance or the systolic and diastolic velocities ratio, for instance, are constant before and after normalization.However, the volumes are no longer absolute values but expressed as percentage of SV, enabling for direct inter-comparison between patients of a very different ventricle size or LVEF.This should be considered when drawing conclusions on the resulting transient patterns, presented in the main manuscript (see Fig.3).Thus, a plausible conclusion from Fig. SII.1 is not that the passive filling is faster in patient 1 but that, considering the rest of the transient velocities, the passive filling is relatively faster in patient 1.

Fig
Fig. SII.1 -LV volume temporal transient normalization for 2 patients.LEFT: LV volume temporal transients expressed in absolute volumes (mL/s).RIGHT: Normalized LV Volume temporal transients (%SV/s).The normalization standardizes for ventricle size and stroke volume, enabling for transient 'shape' inter-comparisons.In consequence, the passive filling peak velocity, considering its smaller SV and the rest of the transient velocities, is relatively faster for patient 2 ( ⃗ > ⃗), while, in absolute terms, this is the opposite ( ⃗ > ⃗).

Fig. SIII. 1 -
Fig. SIII.1 -LEFT: LV Volume transient of patient 692.MIDDLE: The 60 variables that describe the patient volume transient are sorted in columns, one per patient, to apply PCA.RIGHT: Projection of the patients in the 2 PCA directions that contributed the most to multivariate models (see Table3) in order to maximize MACE (red) vs No MACE (blue) differences, that it, Vt AI 3 and Vt AI 5. We moved from 60 to just 2 transient variables per patient, which, additionally, are interpretable.
Fig. SIII.2, if we move in the direction of a mode, we can see how the mean transient curve deforms in its particular way.This allows to describe each LV volume transient as a mean volume transient curve plus the variations encoded by each linear anatomical mode times the amount and direction of variation, or PCA coefficients (See Fig. SIII.4):= + • , where represents the mean volume transient, the anatomical PCA modes, and their respective PCA coefficients.Each of the modes, , is therefore a continuous variable, that accounts forPCA

Fig
Fig. SIII.4 -Following PCA application, each LV volume temporal transient is decomposed into the mean volume transient (average of population of LV transients) plus the anatomical modes (transient variations, illustrated here as mean plus the positive extreme) times the corresponding PCA coefficient, .The figure illustrates the decomposition of patient 692.

Fig. SIV. 2 -
Fig. SIV.2 -Average LV volume transient, normalized by stroke volume, of the AMI cohort (dashed line), along with the average transient stratifying by MACE (red) and no MACE (blue).

Fig
Fig. SV.2 -AUC endpoints prediction results stratifying by LVEF (threshold: 0.35) and applying the LDA models resulting from the backward stepwise analysis (See Table3 and Table SV.1).The labels are presented as subgroup (MACE vs No MACE cases within the subgroup).
SVII.1).The experiments summarized in Table SVII.1 show that any of the two components significantly contributes to the baseline model (cardiovascular risk factors, basic patient characteristics and established CMR markers), as the LDA stepwise selection demonstrates.This is particularly interesting in the systolic component case study, where the mode Vt-S AI 2 is significantly related to MACE in combination with other variables but not if analyzed individually (p = 0.148, see Fig. SVII.1).

Fig
Fig. SVII.1 -AI-derive volume transient features relevant to MACE occurrence prediction, resulting from diastolic component unsupervised learning (Vt-D AI 2, Vt-D AI 4, and Vt-D AI 5) and systolic component analysis (Vt-S AI 5).The MACE (red, class 1) and No MACE (blue, class 0) representations correspond to the 10 th and 90 th percentiles in the LDA direction.This allows to visualize the particular pattern or change encode by each of the unsupervised variables (RR-interval, diastasis, etc.) as well as to describe how a representative MACE and No-MACE volume transient components would theoretically look like according to each of these four unsupervised variables.The P value, resubstitution and leave-one-out AUCs are presented along each mode as MACE and No-MACE distributions, further stratified into infarct aetiology (STEMI and NSTEMI).

P
= 0.008; AUC RS = 0.571; AUC L1 = 0.562 Vt-S AI 2 P = 0.148; AUC RS = 0.567; AUC L1 = 0.561 12 (a) Describe all statistical methods, including those used to control for confounding 10, 11 (b) Describe any methods used to examine subgroups and interactions 11 (c) Explain how missing data were addressed 11 (d) If applicable, explain how matching of cases and controls was addressed numbers of individuals at each stage of study-eg numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential confounders

Table SIV .1 -Basic patient characteristics, cardiovascular risk factors and 3D LV biomarkers
MACE groups.Hazard ratios (HR) presented with 95% confidence intervals and predictor significance.AUC k provides the predictive power of each biomarker, assessed via LDA (median AUC, 10-cross-fold validated, 100 random data splits).MACE: major adverse cardiac events; PCI: percutaneous coronary intervention; TIMI: Thrombolysis in Myocardial Infarction.Reproduced from (18).

Table 2 ,
Table 3, Table SIV.1 (b) Report category boundaries when continuous variables were categorized Table 2, Table 3, Table SIV.1 (c) If relevant, consider translating estimates of relative risk into absolute risk for a meaningful time period

Table 2 ,
Table 3, Table SIV.1,Fig. 5 of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based 22 Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist for casecontrol studies, adapted from (40).(*) The size of the study is discussed in Table SVIII.1.